K-means based group clustering approach for Group Recommender Systems in a metaverse environment

M. W. Geda; Yuk Ming Tang; Sze Chit Leong; C. K.M. Lee; Chung Hin Lai

K-means based group clustering approach for Group Recommender Systems in a metaverse environment

M. W. Geda, Yuk Ming Tang, Sze Chit Leong, C. K.M. Lee, Chung Hin Lai

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

Abstract

In recent years, digital technology has developed rapidly. The importance of hyperpersonalization and marketing automation has been elevated due to the rise of online, virtual shopping environments, spurring the development of intelligent recommender systems. To provide insights for the design and marketing of decision-making in such environments, a clustering method based on K-means is proposed for group recommender systems (GRSs). The proposed method is demonstrated on furniture products, where users are segmented into groups according to product preferences and demographic characteristics. Results show that out-group heterogeneity in terms of demographic variables and product attributes can be identified among user groups. Such information is crucial when implementing targeted marketing campaigns. In addition, the results can provide designers with insights about the most preferred design attributes.

Original language	English
Title of host publication	International Conference on Design and Semantics of Form and Movement
Editors	Miguel Bruns, Lin-Lin Chen, Jun Hu, Sara Colombo, Yihyun Lim, Steven Kyffin, Ozcan Vieira, E. Jeroen Raijmakers, Lucia Rampino, Edgar Rodriguez Ramirez, Dagmar Johanna Steffen, Calvin Wong
Publisher	The Hong Kong Polytechnic University, Hong Kong
Pages	62-68
Number of pages	7
ISBN (Print)	9789623678704
Publication status	Published - Jul 2023
Event	12th International Conference on Design and Semantics of Form and Movement, DeSForM 2023 - Hong Kong, Hong Kong Duration: 5 Jul 2023 → 7 Jul 2023

Publication series

Name	International Conference on Design and Semantics of Form and Movement
ISSN (Electronic)	2706-6150

Conference

Conference	12th International Conference on Design and Semantics of Form and Movement, DeSForM 2023
Country/Territory	Hong Kong
City	Hong Kong
Period	5/07/23 → 7/07/23

Keywords

Group clustering
k-means Clustering
Product design, Personalization
Recommender System

ASJC Scopus subject areas

Arts and Humanities (miscellaneous)
Human-Computer Interaction
Mechanics of Materials

Cite this

Geda, M. W., Tang, Y. M., Leong, S. C., Lee, C. K. M., & Lai, C. H. (2023). K-means based group clustering approach for Group Recommender Systems in a metaverse environment. In M. Bruns, L.-L. Chen, J. Hu, S. Colombo, Y. Lim, S. Kyffin, O. Vieira, E. J. Raijmakers, L. Rampino, E. R. Ramirez, D. J. Steffen, & C. Wong (Eds.), International Conference on Design and Semantics of Form and Movement (pp. 62-68). (International Conference on Design and Semantics of Form and Movement). The Hong Kong Polytechnic University, Hong Kong.

Geda, M. W. ; Tang, Yuk Ming ; Leong, Sze Chit et al. / K-means based group clustering approach for Group Recommender Systems in a metaverse environment. International Conference on Design and Semantics of Form and Movement. editor / Miguel Bruns ; Lin-Lin Chen ; Jun Hu ; Sara Colombo ; Yihyun Lim ; Steven Kyffin ; Ozcan Vieira ; E. Jeroen Raijmakers ; Lucia Rampino ; Edgar Rodriguez Ramirez ; Dagmar Johanna Steffen ; Calvin Wong. The Hong Kong Polytechnic University, Hong Kong, 2023. pp. 62-68 (International Conference on Design and Semantics of Form and Movement).

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title = "K-means based group clustering approach for Group Recommender Systems in a metaverse environment",

abstract = "In recent years, digital technology has developed rapidly. The importance of hyperpersonalization and marketing automation has been elevated due to the rise of online, virtual shopping environments, spurring the development of intelligent recommender systems. To provide insights for the design and marketing of decision-making in such environments, a clustering method based on K-means is proposed for group recommender systems (GRSs). The proposed method is demonstrated on furniture products, where users are segmented into groups according to product preferences and demographic characteristics. Results show that out-group heterogeneity in terms of demographic variables and product attributes can be identified among user groups. Such information is crucial when implementing targeted marketing campaigns. In addition, the results can provide designers with insights about the most preferred design attributes.",

keywords = "Group clustering, k-means Clustering, Product design, Personalization, Recommender System",

author = "Geda, {M. W.} and Tang, {Yuk Ming} and Leong, {Sze Chit} and Lee, {C. K.M.} and Lai, {Chung Hin}",

note = "Publisher Copyright: {\textcopyright} 2023, The Hong Kong Polytechnic University. All rights reserved.; 12th International Conference on Design and Semantics of Form and Movement, DeSForM 2023 ; Conference date: 05-07-2023 Through 07-07-2023",

year = "2023",

month = jul,

language = "English",

isbn = "9789623678704",

series = "International Conference on Design and Semantics of Form and Movement",

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editor = "Miguel Bruns and Lin-Lin Chen and Jun Hu and Sara Colombo and Yihyun Lim and Steven Kyffin and Ozcan Vieira and Raijmakers, {E. Jeroen} and Lucia Rampino and Ramirez, {Edgar Rodriguez} and Steffen, {Dagmar Johanna} and Calvin Wong",

booktitle = "International Conference on Design and Semantics of Form and Movement",

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Geda, MW, Tang, YM, Leong, SC, Lee, CKM & Lai, CH 2023, K-means based group clustering approach for Group Recommender Systems in a metaverse environment. in M Bruns, L-L Chen, J Hu, S Colombo, Y Lim, S Kyffin, O Vieira, EJ Raijmakers, L Rampino, ER Ramirez, DJ Steffen & C Wong (eds), International Conference on Design and Semantics of Form and Movement. International Conference on Design and Semantics of Form and Movement, The Hong Kong Polytechnic University, Hong Kong, pp. 62-68, 12th International Conference on Design and Semantics of Form and Movement, DeSForM 2023, Hong Kong, Hong Kong, 5/07/23.

K-means based group clustering approach for Group Recommender Systems in a metaverse environment. / Geda, M. W.; Tang, Yuk Ming; Leong, Sze Chit et al.
International Conference on Design and Semantics of Form and Movement. ed. / Miguel Bruns; Lin-Lin Chen; Jun Hu; Sara Colombo; Yihyun Lim; Steven Kyffin; Ozcan Vieira; E. Jeroen Raijmakers; Lucia Rampino; Edgar Rodriguez Ramirez; Dagmar Johanna Steffen; Calvin Wong. The Hong Kong Polytechnic University, Hong Kong, 2023. p. 62-68 (International Conference on Design and Semantics of Form and Movement).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

TY - GEN

T1 - K-means based group clustering approach for Group Recommender Systems in a metaverse environment

AU - Geda, M. W.

AU - Tang, Yuk Ming

AU - Leong, Sze Chit

AU - Lee, C. K.M.

AU - Lai, Chung Hin

PY - 2023/7

Y1 - 2023/7

N2 - In recent years, digital technology has developed rapidly. The importance of hyperpersonalization and marketing automation has been elevated due to the rise of online, virtual shopping environments, spurring the development of intelligent recommender systems. To provide insights for the design and marketing of decision-making in such environments, a clustering method based on K-means is proposed for group recommender systems (GRSs). The proposed method is demonstrated on furniture products, where users are segmented into groups according to product preferences and demographic characteristics. Results show that out-group heterogeneity in terms of demographic variables and product attributes can be identified among user groups. Such information is crucial when implementing targeted marketing campaigns. In addition, the results can provide designers with insights about the most preferred design attributes.

AB - In recent years, digital technology has developed rapidly. The importance of hyperpersonalization and marketing automation has been elevated due to the rise of online, virtual shopping environments, spurring the development of intelligent recommender systems. To provide insights for the design and marketing of decision-making in such environments, a clustering method based on K-means is proposed for group recommender systems (GRSs). The proposed method is demonstrated on furniture products, where users are segmented into groups according to product preferences and demographic characteristics. Results show that out-group heterogeneity in terms of demographic variables and product attributes can be identified among user groups. Such information is crucial when implementing targeted marketing campaigns. In addition, the results can provide designers with insights about the most preferred design attributes.

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KW - k-means Clustering

KW - Product design, Personalization

KW - Recommender System

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M3 - Conference article published in proceeding or book

AN - SCOPUS:85190714473

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T3 - International Conference on Design and Semantics of Form and Movement

SP - 62

EP - 68

BT - International Conference on Design and Semantics of Form and Movement

A2 - Bruns, Miguel

A2 - Chen, Lin-Lin

A2 - Hu, Jun

A2 - Colombo, Sara

A2 - Lim, Yihyun

A2 - Kyffin, Steven

A2 - Vieira, Ozcan

A2 - Raijmakers, E. Jeroen

A2 - Rampino, Lucia

A2 - Ramirez, Edgar Rodriguez

A2 - Steffen, Dagmar Johanna

A2 - Wong, Calvin

PB - The Hong Kong Polytechnic University, Hong Kong

T2 - 12th International Conference on Design and Semantics of Form and Movement, DeSForM 2023

Y2 - 5 July 2023 through 7 July 2023

ER -

Geda MW, Tang YM, Leong SC, Lee CKM, Lai CH. K-means based group clustering approach for Group Recommender Systems in a metaverse environment. In Bruns M, Chen LL, Hu J, Colombo S, Lim Y, Kyffin S, Vieira O, Raijmakers EJ, Rampino L, Ramirez ER, Steffen DJ, Wong C, editors, International Conference on Design and Semantics of Form and Movement. The Hong Kong Polytechnic University, Hong Kong. 2023. p. 62-68. (International Conference on Design and Semantics of Form and Movement).

K-means based group clustering approach for Group Recommender Systems in a metaverse environment

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Keywords

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